Currently there exist many systems designed to perform textual or other engine search. With the rapid growth of the internet and users of the internet over the past ten years, a rapid increase in the amount of information available over the internet has developed. Many UNIVERSAL search engines, such as Google, Yahoo, AltaVista, Rambler, etc. are available to users and provide powerful search tools for general use. These search engines enable any user to query the wide repository of public web-based documents that are indexed by these systems. Search engines propose different strategies from one another in attempting to find documents which are most relevant to the user-specified search criteria. Another way of attempting to receive relevant documents is by filtering, wherein an interface is provided to allow the user to set parameters to arrive at a set of relevant documents. However, the large volume of available data causes an undesirable result in many of these general searches as most simple searches return large number of documents, many of which are not useful or not relevant to that which the user is seeking. On the other hand, if a user defines its request in an extremely detailed manner (e.g., including years, country, type of information, etc.), the system typically returns a low number of found documents, but some important documents may be omitted. To provide compromise between width of query and amount of received documents, these search engine systems allow the use of different auxiliary tools, e.g.:                Automatic “and queries”: by default, universal search engine systems return only documents that satisfy all of mentioned search terms        Exclusion of some words: The ability to return pages which do not include specific terms)        Automatic exclusion of common words: The ability to ignore “common” words and characters such as “where” and “how”, as well as certain single digits and single letters        Search within results: The ability to offer a search on previously found documents        Negative terms: The ability to focus a search only by words related to a selected meaning of a word and to avoid other possible meanings if a search term has more than one meaning        Positive terms: The ability to include an essential common word term if it is required for getting the results        To define order of words        To use function “one word is immediately after other word”        To use function “words on the common phrase”        Stemming (when appropriate, it will search not only for search terms, but also for words that are similar to some or all of those terms).        Lemmatization (words are increased from their canonical form, e.g., infinitive for verbs; thus, when appropriate, it will search not only for search terms, but also for words that are similar).        Phrase searches (if only want results that include an exact phrase are suitable).        Synonym search (if it is necessary to search not only for search term but also for it's synonyms)        “OR” search (to find pages that include either of two search terms)        Domain search (to search only within one specific website)        Occurrences (specify where search terms have to be occurring on the page—anywhere on the page, in the title, in links to the page, etc.)        Similar pages (to find pages that are related for a particular result)        Number of results (to see more results per page, that it is defined for default)        Language (to return pages written in any language or in some specific language)        File Format (to return pages on any format or on some specific format)        Date (to return pages updated in anytime or in some specific time interval)        Domain (to return pages from the specific site or domain)        Topic (to perform search in some specific topic)        Etc.        
Nevertheless even these tools sometimes do not allow receiving some required documents. A common drawback of these universal search engine systems is that they do not allow getting a feedback from the user about the extent of success (or lack of success) of search which were performed earlier and to use this information for further “more thorough” search.
Some “corporative”, domain-oriented specialized search engine systems (e.g., travel domain, education domain, real-estate domain, etc.) use feedback from users. Some web-based search engines use data mining capabilities. Such systems use unsupervised clustering to group documents by similar topics. According to a single query such systems are built to search “nearest” (from clustering point of view) documents and get them to the user. The unsupervised clustering procedure employs a group-average-linkage technique to determine relative distances between documents. Such systems take off-line into account behavior of similar users in the past, but they don't allow taking into account on-line, dynamic profile of the actual specific user.
A major limitation of these prior art approaches however, is their inability to apply learning procedures on-line and specifically for a given user/user's point of view, to improve search and selection outcome first of all for a given particular individual and his/her desired context, not for associated group of users on similar topic. The prior art approaches are also limited in their ability to apply only on corporative, specialized search engine systems.